Machine Learning-Based Correlation Framework for Flow Condensation in Horizontal Channels

Abstract

This computational study proposes utilizing the advantages of machine learning algorithms to develop a flow condensation model for horizontal smooth channels. A diverse database of 6,532 data samples, encompassing 27 pure refrigerant fluids, is utilized to model the proposed condensation heat transfer coefficient. Nine machine learning algorithms founded upon intelligently devised mathematical methods have been applied to the vast database to extract the most beneficial non-dimensional parameters to model the convective condensation heat transfer coefficient. Between the trained machine learning models, Extreme Gradient Boosting and deep neural network algorithms provide the best estimations, with respective coefficient determination values of 0.9874 and 0.9822. The most influential dimensionless numbers derived from the Extreme Gradient Boosting algorithm, which produces the most accurate estimations, are used to develop a novel heat transfer coefficient model for flow condensation. With a corresponding mean absolute error value of 15.92% and mean relative error value of -2.38%, it achieves much better predictions than those obtained from the literature on convective flow condensation. The validity of the projections extracted from the proposed correlation has also been analyzed based on the excluded database, and the superiority of the heat transfer model in extrapolating the experimental data is successfully verified.